LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering

📰 ArXiv cs.AI

LLM4CodeRE uses generative AI for code decompilation analysis and reverse engineering of malicious software

advanced Published 8 Apr 2026
Action Steps
  1. Train a large language model on a dataset of malicious software to adapt to the domain
  2. Use the trained model to translate low-level representations into high-level source code
  3. Apply the model to code decompilation analysis and reverse engineering tasks to identify and understand malicious code
  4. Fine-tune the model on specific types of malware to improve its performance and accuracy
Who Needs to Know This

Security researchers and malware analysts on a team can benefit from LLM4CodeRE to improve their code decompilation and reverse engineering tasks, and software engineers can use it to analyze and understand obfuscated code

Key Insight

💡 Domain-adapted large language models can be effective in code decompilation analysis and reverse engineering of malicious software

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💡 Generative AI for code decompilation analysis and reverse engineering of malicious software!

Key Takeaways

LLM4CodeRE uses generative AI for code decompilation analysis and reverse engineering of malicious software

Full Article

Title: LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering

Abstract:
arXiv:2604.06095v1 Announce Type: cross Abstract: Code decompilation analysis is a fundamental yet challenging task in malware reverse engineering, particularly due to the pervasive use of sophisticated obfuscation techniques. Although recent large language models (LLMs) have shown promise in translating low-level representations into high-level source code, most existing approaches rely on generic code pretraining and lack adaptation to malicious software. We propose LLM4CodeRE, a domain-adapti
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